Viola and Jones (VJ) cascade classification methods 
have proven to be very successful
in detecting objects belonging to a single class --- e.g., faces.
This paper addresses the more challenging ``many class detection'' problem:
detecting and identifying objects that belong to 
any of a set of classes.
We use a set of learned weights 
(corresponding to the parameters of a set of binary linear separators)
to identify these objects.
We show that objects within many real-world classes
tend to form clusters in this induced ``classifier space''.
As the result of a sequence of classifiers can suggest a possible label for
each object, 
we formulate this task as a Markov Decision Process.
Our system first uses a ``decision tree classifier''
(i.e., a policy produced using dynamic programming)
to specify when to apply which classifier
to produce a possible class label for each sub-image W of a test image.
This corresponds to a leaf of the decision tree.
It then uses a cascade of classifiers, specific to this leaf
to confirm that W is an instance of the proposed class.
We present empirical evidence to verify that our ideas work effectively:
showing that our system is essentially as accurate as running a set of cascade
classifiers (one for each class of objects),
but is much faster than that approach.